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About miHub®
Maximising Researchers' Exploration and Achievements through Materials Informatics
Vision
Turning Data and Knowledge
into Digital Assets
With intuitive data analysis functions, miHub® seamlessly supports everything from organising experimental data to accumulating new knowledge. While leveraging domain expertise, it drives data-driven hypothesis testing using statistical and machine learning techniques. This deepens the understanding of research themes and guides researchers towards their next breakthrough steps.
Furthermore, miHub® enables the accumulation of development-related data and know-how in reusable forms, fostering digital utilisation across the entire organisation. Combined with comprehensive support for developers, it dramatically accelerates R&D powered by Materials Informatics.
Conventional Approach
Before
Data-Driven Approach
After
miHub® transforms accumulated data and knowledge into digital assets, empowering researchers to deepen and broaden their exploration.
Solutions
01Integrated Data Workflow
Integrated Data Workflow
Streamline data organisation across development teams and seamlessly convert it into MI-ready datasets. Manage every step of experimental planning—data registration, analysis setup, experimental design, and result interpretation—through an intuitive workflow.
This prevents data organisation from becoming an end in itself and creates an environment where researchers and teams can focus their energy on the creative aspects of development, such as analysis, interpretation and idea generation.
02Design of Experiment
Design of Experiment
Bayesian optimisation and machine learning accelerate the exploration of experimental points, while deepening understanding of development themes. By acquiring high-value data, researchers gain richer insights and maximise the impact of their data.
For newcomers, miHub guides exploration and reflection, fostering habits of scientific reasoning. For experts, it offers the freedom to design analyses that integrate their domain knowledge, enabling higher-impact results.
03Data Intelligence (DI)
Data Intelligence (DI)
Integrating development themes with analysis projects and linking them to verification items provides a holistic view of experimental data and related information. Visualisation of factor relationships helps researchers intuitively identify what to analyse and validate.
By uncovering hidden structures and causal links in complex data, it directs focus quickly to the most critical and influential factors.
04Knowledge Co-creation
Knowledge Co-creation
Because miHub links analyses, experiments and interpretations and records them with the underlying data, activities can be shared directly in the tool. This streamlines collaboration and helps everyone grasp project context and progress immediately.
By making analysis paths easy to trace, tacit know-how can be shared across the team and MI adoption spreads naturally. Data shifts from individual possession to a shared organisational asset.
05Success Enablement
Success Enablement
Dedicated data scientists support each R&D theme, helping researchers and analysts use miHub® with confidence. Training and seminars are combined as needed to build skills effectively.
With experience supporting over 100 companies, we provide best practices for promoting and embedding MI according to your organisation and development stage. A support framework tailored to each company drives continuous growth and sustained results.
Integrated Data Workflow
Streamline data organisation across development teams and seamlessly convert it into MI-ready datasets. Manage every step of experimental planning—data registration, analysis setup, experimental design, and result interpretation—through an intuitive workflow.
This prevents data organisation from becoming an end in itself and creates an environment where researchers and teams can focus their energy on the creative aspects of development, such as analysis, interpretation and idea generation.
Design of Experiment
Bayesian optimisation and machine learning accelerate the exploration of experimental points, while deepening understanding of development themes. By acquiring high-value data, researchers gain richer insights and maximise the impact of their data.
For newcomers, miHub guides exploration and reflection, fostering habits of scientific reasoning. For experts, it offers the freedom to design analyses that integrate their domain knowledge, enabling higher-impact results.
Data Intelligence (DI)
Integrating development themes with analysis projects and linking them to verification items provides a holistic view of experimental data and related information. Visualisation of factor relationships helps researchers intuitively identify what to analyse and validate.
By uncovering hidden structures and causal links in complex data, it directs focus quickly to the most critical and influential factors.
Knowledge Co-creation
Because miHub links analyses, experiments and interpretations and records them with the underlying data, activities can be shared directly in the tool. This streamlines collaboration and helps everyone grasp project context and progress immediately.
By making analysis paths easy to trace, tacit know-how can be shared across the team and MI adoption spreads naturally. Data shifts from individual possession to a shared organisational asset.
Success Enablement
Dedicated data scientists support each R&D theme, helping researchers and analysts use miHub® with confidence. Training and seminars are combined as needed to build skills effectively.
With experience supporting over 100 companies, we provide best practices for promoting and embedding MI according to your organisation and development stage. A support framework tailored to each company drives continuous growth and sustained results.
Features
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Bayesian Optimisation
It enables the discovery of optimal conditions with fewer experiments. By efficiently exploring the search space, researchers can strategically acquire high-value data and gain useful insights that lead to improved material properties and process optimisation.
Backed by a development team whose research has been presented at top international conferences in artificial intelligence, we deliver practical and highly reliable technology.
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Predictive Modelling for Candidate Exploration
We build predictive models from experimental data to evaluate candidate points in a comprehensive manner. Techniques for visualising high-dimensional spaces reveal hidden structures and patterns, enhancing the understanding of results. Model interpretation further provides insights into factor relationships and trade-offs.
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Feature Generation and Data Management
User-friendly experimental tables can be quickly transformed into MI-ready datasets, reducing complex data wrangling and speeding up data utilisation.
Material structures can be used to automatically generate properties and features, increasing data resolution. By linking to relevant databases and intermediate properties such as material characteristics or process descriptors, miHub enables unified and efficient management of experimental data. -
Development Management
Connect verification items with analysis projects to manage overall progress in an integrated way. Gain a holistic view of data and results, share insights, and visualise factor relationships for faster, more strategic decisions. Shared progress across the team builds the foundation for seamless collaboration.
and more...
User Voices
